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genetic algorithm in artificial intelligence | genetic algorithm in hindi | Artificial intelligence
 
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Hey friends welcome to well academy here is the topic genetic algorithm in artificial intelligence in hindi DBMS Gate Lectures Full Course FREE Playlist : https://goo.gl/Z7AAyV Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy genetic algorithm in artificial intelligence, genetic algorithm in artificial intelligence in hindi, genetic algorithm in artificial intelligence example, genetic algorithm in artificial intelligence tutorial, genetic algorithm in artificial intelligence in urdu, genetic algorithm in artificial intelligence hindi, genetic algorithm in hindi, genetic algorithm in ai, genetic algorithm artificial intelligence, genetic algorithm, genetic algorithm ai, genetic algorithm well academy, genetic algorithm crossover genetic algorithm tutorial genetic algorithm example genetic algorithm genetic algorithm fitness function genetic algorithm artificial intelligence artificial intelligence well academy well academy artificial intelligence artificial intelligence tutorial artificial intelligence in hindi artificial intelligence lecture artificial intelligence lecture in hindi
Views: 57683 Well Academy
Introduction to Genetic Algorithms
 
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A brief introduction to genetic algorithms with examples.
Views: 125725 chriskam1250
Genetic Algorithm | Artificial Intelligence Tutorial in Hindi Urdu | Genetic Algorithm Example
 
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#askfaizan | #syedfaizanahmad PlayList : Artificial Intelligence : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH Genetic Algorithm is - Optimization Algorithm - Based on natural phenomenon - Nature inspired approach based on Darwin’s law of Survival of the fittest and  bio-inspired operators such as Pairing Crossover and Mutation. - frequently used to find optimal or near-optimal solutions to difficult problems Optimization- is the process of making something better Terminology - Population - Chromosomes  - Gene Operators are - Selection - Crossover - Mutation for Complete Artificial Intelligence Videos click on the link : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/askfaizan1/ Instagram page : https://www.instagram.com/ask_faizan/ Twitter : https://twitter.com/ask_faizan/
Views: 3201 Ask Faizan
Automatic Time Table Generation Using Genetic Algorithm
 
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Title: Automatic Time Table Generation Using Genetic Algorithm Domain: Data Mining Key Features: 1. Generation of time table using genetic algorithm. 2. Time table generation separately for teacher and students. 3. Downloadable in .xls file 4. Facility of curd model for teacher and students, etc. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2016 – 2017 data mining projects 5. 2016 – 2017 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2016 – 2017 ieee titles 8. 2016 – 2017 base paper 9. 2016 – 2017 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2016 – 2017 data mining weka projects 13. 2016 – 2017 b.e projects 14. 2016 – 2017 m.e projects 15. 2016 – 2017 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2016 – 2017 ieee base paper free download 23. 2016 – 2017 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2016 - 2017 48. 2016 - 2017 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Views: 12428 InnovationAdsOfIndia
9.1: Genetic Algorithm: Introduction - The Nature of Code
 
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Welcome to part 1 of a new series of videos focused on Evolutionary Computing, and more specifically, Genetic Algorithms. In this tutorial, I introduce the concept of a genetic algorithm, how it can be used to approach "search" problems and how it relates to brute force algorithms. Support this channel on Patreon: https://patreon.com/codingtrain Send me your questions and coding challenges!: https://github.com/CodingTrain/Rainbow-Topics Contact: https://twitter.com/shiffman Links discussed in this video: The Nature of Code: http://natureofcode.com/ BoxCar2D: http://boxcar2d.com/ Source Code for the Video Lessons: https://github.com/CodingTrain/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For More Genetic Algorithm videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV For More Nature of Code videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6aFlwukCmDf0-1-uSR7mklK Help us caption & translate this video! http://amara.org/v/Sld6/
Views: 178729 The Coding Train
What is a Genetic Algorithm
 
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Get an introduction to the components of a genetic algorithm. Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 Ready to Buy: https://goo.gl/vsIeA5 Learn more Genetic Algorithms: https://goo.gl/kYxNPo Learn how genetic algorithms are used to solve optimization problems. Examples illustrate important concepts such as selection, crossover, and mutation. Finally, an example problem is solved in MATLAB® using the ga function from Global Optimization Toolbox.
Views: 109432 MATLAB
Dayun Zig Z1!!! Lyra2REv2 algorithm miner !!! Overview - Semalt
 
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Views: 24 Prashant Badole
Neuro-Fuzzy Hybrid System - Soft Computing ~xRay Pixy
 
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If you want to download PPT than you can easily download it now from: http://xraypixy.com I recreated this video on NFHS with improvement in voice and stuff. and I also added information about machine learning techniques. - Neural network - Role of NN in machine learning - Neural network learning algorithms - The neural network in pattern recognition, speech and object recognition. - The Comparision of Neural network with the biological network. video link(Neuro-Fuzzy Hybrid System (Part-2)- Soft Computing) https://www.youtube.com/watch?v=Bv7EtS6q6_Q Soft Computing book for the Neural network:- https://www.amazon.in/Principles-Soft-Computing-2ed-WIND/dp/8126527412/ref=as_li_ss_tl?s=books&ie=UTF8&qid=1513193138&sr=1-1&keywords=soft+computing&linkCode=sl1&tag=pixy-21&linkId=4bcc64ac49ca9f9acf69edb48db2bbcd Neuro-Fuzzy Hybrid System A Hybrid Intelligent System is one that combines at least two intelligent technologies. For example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system. Neuro-Fuzzy hybridization is widely termed as Fuzzy Neural Network (FNN) or Neuro-Fuzzy System(NFS). NFHS is a learning mechanism that utilizes the training and learning algorithms from neural networks to find parameters of a fuzzy system. The architecture is a three-layer feedforward neural network model. if you want to ask anything related this video than you can ask me on this page : - Google+ page: https://plus.google.com/118027282802701170491 Full artical: http://pixycomputer.blogspot.com/2017/03/neuro-fuzzy-hybrid-system-in-soft.html Ritika xRay Pixy (www.xraypixy.com)
Views: 17055 Ritika Saini
Optimized Association Rule Mining with Genetic Algorithms
 
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The mechanism for unearthing hidden facts in large datasets and drawing inferences on how a subset of items influences the presence of another subset is known as Association Rule Mining (ARM). There is a wide variety of rule interestingness metrics that can be applied in ARM. Due to the wide range of rule quality metrics it is hard to determine which are the most `interesting' or `optimal' rules in the dataset. In this paper we propose a multi-objective approach to generating optimal association rules using two new rule quality metrics: syntactic superiority and transactional superiority. These two metrics ensure that dominated but interesting rules are returned to not eliminated from the resulting set of rules.
Optimization with Genetic Algorithm - A MATLAB Tutorial for beginners
 
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In this tutorial, I will show you how to optimize a single objective function using Genetic Algorithm. We use MATLAB and show the whole process in a very easy and understandable step-by-step process. For a tutorial on Constrained Optimization with Genetic Algorithm see this video: https://www.youtube.com/watch?v=esGZtl3gIlY&feature=youtu.be
Views: 60964 NKN DNE
Mod-01 Lec-38 Genetic Algorithms
 
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Design and Optimization of Energy Systems by Prof. C. Balaji , Department of Mechanical Engineering, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 144071 nptelhrd
Neural Networks in Data Mining | MLP Multi layer Perceptron Algorithm in Data Mining
 
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Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified cases Steps of Classification: 1. Model construction: Describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute. The set of tuples used for model construction is training set. The model is represented as classification rules, decision trees, or mathematical formulae. 2. Model usage: For classifying future or unknown objects Estimate accuracy of the model If the accuracy is acceptable, use the model to classify new data MLP- NN Classification Algorithm The MLP-NN algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer. Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted and fed simultaneously to a second layer of “neuronlike” units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples. Algorithm of MLP-NN is as follows: Step 1: Initialize input of all weights with small random numbers. Step 2: Calculate the weight sum of the inputs. Step 3: Calculate activation function of all hidden layer. Step 4: Output of all layers For more information and query visit our website: Website : http://www.e2matrix.com Blog : http://www.e2matrix.com/blog/ WordPress : https://teche2matrix.wordpress.com/ Blogger : https://teche2matrix.blogspot.in/ Contact Us : +91 9041262727 Follow Us on Social Media Facebook : https://www.facebook.com/etwomatrix.researchlab Twitter : https://twitter.com/E2MATRIX1 LinkedIn : https://www.linkedin.com/in/e2matrix-training-research Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar Pinterest : https://in.pinterest.com/e2matrixresearchlab/ Tumblr : https://www.tumblr.com/blog/e2matrix24
Cancer Identification Data Mining Java Project
 
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Cancer Identification System Data Mining Java Project Download Project Code, Report and PPT :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 770 1000 Projects
Intrusion Detection based on KDD Cup Dataset
 
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Final Presentation for Big Data Analysis
Views: 6965 Qiankun Zhuang
Seminar on Neural Network - Datamining
 
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Presented by Karthik A
Views: 977 Karthik Gowda
Genetic Algorithms
 
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A simple explanation of how genetic algorithms work.
Views: 166336 Andrew Fields
Optimisation in MatLab Simulink
 
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In this video I'm showing how to perform an optimisation procedure in MatLab Simulink using custom requirement http://join.air.io/mlevinskyi
Views: 3456 Maksym Levinskyi
Working of Genetic Algorithm in AI Part -2
 
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This tutorial has steps in genetic algorithm
Views: 10213 Red Apple Tutorials
An Introduction to Fuzzy Logic
 
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This video quickly describes Fuzzy Logic and its uses for assignment 1 of Dr. Cohen's Fuzzy Logic Class.
Views: 184079 Tim Arnett
Mod-01 Lec-39 Genetic Algorithms contd...
 
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Design and Optimization of Energy Systems by Prof. C. Balaji , Department of Mechanical Engineering, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 55708 nptelhrd
How SVM (Support Vector Machine) algorithm works
 
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In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share
Views: 463063 Thales Sehn Körting
fuzzy logic in artificial intelligence in hindi | introduction to fuzzy logic example | #28
 
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fuzzy logic in artificial intelligence in hindi | fuzzy logic example | #28 Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO. The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO. The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as − CERTAINLY YES POSSIBLY YES CANNOT SAY POSSIBLY NO CERTAINLY NO well,academy,Fuzzy logic in hindi,fuzzy logic in artificial intelligence in hindi,artificial intelligence fuzzy logic,fuzzy logic example,fuzzy logic in artificial intelligence,fuzzy logic with example,fuzzy logic in artificial intelligence in hindi with exapmle,fuzzy logic,what is fuzzy logic in hindi,what is fuzzy logic with example,introduction to fuzzy logic
Views: 86836 Well Academy
FUZZY LOGIC Introduction. Fuzzy set theory in Hindi/Urdu. Lec-1
 
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Fuzzy logic introduction. Fuzzy Set Theory.
Top 5 Algorithms used in Data Science | Data Science Tutorial | Data Mining Tutorial | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course “Data Science”. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 95372 edureka!
Population based methods for Optimization
 
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Artificial Intelligence by Prof. Deepak Khemani,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 20266 nptelhrd
Algorithms for Big Data (COMPSCI 229r), Lecture 1
 
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Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.
Views: 82706 Harvard University
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
Views: 346522 Thales Sehn Körting
Beginner Intro to Neural Networks 1: Data and Graphing
 
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Hey everyone! This is the first in a series of videos teaching you everything you could possibly want to know about neural networks, from the math behind them to how to create one yourself and use it solve your own problems! This video is meant to be an a quick intro to what neural nets can do, and get us rolling with a simple dataset and problem to solve. In the next video I'll cover how to use a neural network to automate the task our farmer character solves manually here.
Views: 141566 giant_neural_network
matlab image segmentation using genetic algorithm||Engineering Project Consultancy at bangalore
 
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Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis
 
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Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis +91-9994232214,8144199666, [email protected], www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2014 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:[email protected] http://ieee.projectsieee.com/Cloud-Computing http://ieee.projectsieee.com/Data-Mining http://ieee.projectsieee.com/Android http://ieee.projectsieee.com/Image-Processing http://ieee.projectsieee.com/Networking http://ieee.projectsieee.com/Network-Security http://ieee.projectsieee.com/Mobile-Computing http://ieee.projectsieee.com/Parallel-Distributed http://ieee.projectsieee.com/Wireless-Communication http://ieee.projectsieee.com/NS2-Projects http://ieee.projectsieee.com/Matlab Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support
Views: 116 PROJECTS2014
Neural Network Explained -Artificial Intelligence - Hindi
 
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Neural network in ai (Artificial intelligence) Neural network is highly interconnected network of a large number of processing elements called neuron architecture motivated from brain. Neuron are interconnected to synapses which provide input from other neurons which intern provides output i.e input to other neurons. Neuron are in massive therefore they provide distributed network. Extra Tags neural networks nptel, neural networks in artificial intelligence, neural networks in hindi, neural networks and deep learning, neural networks in r, neural networks in ai, neural networks andrew ng, neural networks in python, neural networks mit, neural networks and fuzzy logic, neural networks, neural networks tutorial, neural networks and deep learning coursera, neural networks applications, neural networks api, neural networks ai, neural networks algorithm, neural networks andrej karpathy, neural networks artificial intelligence, neural networks basics, neural networks brain, neural networks backpropagation, neural networks backpropagation example, neural networks biology, neural networks by rajasekaran free download, neural networks backpropagation tutorial, neural networks blockchain, neural networks basics pdf, neural networks bias, neural networks course, neural networks car, neural networks caltech, neural networks computerphile, neural networks demystified, neural networks demo, neural networks demystified part 1 data and architecture, neural networks data mining, neural networks demystified part 1, neural networks deep learning, neural networks demystified part 3, neural networks demystified part 2, neural networks data analytics, neural networks documentary, neural networks example, neural networks explained, neural networks edureka, neural networks explained simply, neural networks explanation, neural networks evolution, neural networks eli5, neural networks explained simple, neural networks for image recognition, neural networks for dummies, neural networks for recommender systems, neural networks for machine learning youtube, neural networks geoffrey hinton, neural networks game, neural networks google, neural networks gradient, neural networks gradient descent, neural networks genetic algorithms, neural networks gesture recognition, neural networks generations, neural networks graphics, neural networks playing games, neural networks hinton, neural networks hugo larochelle, neural networks harvard, neural networks hardware implementation, neural networks how it works, neural networks handwriting recognition, neural networks human brain, neural networks how they work, neural networks hidden units, neural networks hidden layer, neural networks in data mining, neural networks in machine learning, neural networks introduction, neural networks in tamil, neural networks in c++, neural networks java, neural networks java tutorial, neural networks javascript, neural networks jmp, neural networks js, jeff heaton neural networks, introduction to neural networks for java, neural networks khan academy, neural networks knime, recurrent neural networks keras, neural networks for kids, neural networks lecture, neural networks lecture notes, neural networks learn, neural networks linear regression, neural networks logistic regression, neural networks lstm, neural networks learning algorithms, neural networks lecture videos, neural networks lottery prediction, neural networks loss, neural networks machine learning, neural networks matlab, neural networks matlab tutorial, neural networks mathematics, neural networks music, neural networks mit opencourseware, neural networks math, neural networks meaning in tamil, neural networks mit ocw, neural networks nlp, neural networks nptel videos, neural networks numericals, neural networks ng, neural networks natural language processing, backpropagation in neural networks nptel, andrew ng neural networks, neural networks ocw, neural networks on fpga, neural networks ocr, neural networks perceptron, neural networks python tutorial, neural networks ppt, neural networks ppt download, neural networks questions and answers, neural networks robot, neural networks radiology, neural networks regularization, neural networks recurrent, neural networks rapidminer, neural networks using r, neural networks stanford, neural networks siraj, neural networks spss, neural networks sigmoid function, neural networks simple, neural networks simplified, neural networks sentdex, neural networks siraj raval, neural networks stock market, neural networks simulation, neural networks training, neural networks ted, neural networks tensorflow, neural networks types, neural networks tensorflow tutorial, neural networks tutorial python, neural networks trading, neural networks tutorial youtube,tworks 1, neural networks 2016, neural networks 3blue1brown, neural networks 3d, neural networks 3d reconstruction, neural networks in 4 minutes, lecture 9 - neural networks
Views: 6474 CaelusBot
Detecting E Banking Phishing Websites Using Associative Classification
 
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System uses efficient data mining algorithms to detect and warn user about e banking phishing websites.
Views: 6181 Nevon Projects
Machine Learning Lecture 2: Sentiment Analysis (text classification)
 
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In this video we work on an actual sentiment analysis dataset (which is an instance of text classification), for which I also provide Python code (see below). The approach is very similar to something that is commonly called a Naive Bayes Classifier. Website associated with this video: http://karpathy.ca/mlsite/lecture2.php
Views: 49698 MLexplained
Introduction to WebMining - Part 1
 
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Introduction to Web Mining and its usage in E-Commerce Websites. This is part 1. This will contain introduction of the field and in part two we will discuss its usage in E-Commerce website. Please don't forget to give your feedback... :)
Views: 3948 zdev log
Interview with Prof. Dr. Thomas Bäck -- about using Data Mining to create Innovations
 
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Short Interview with Prof. Dr. Thomas Bäck about using Data Mining for Innovations. e.g. genetic / evolutionary algorithms to solve multi-objective problems. Thomas is CEO of DIVIS www.divis-gmbh.de and I met him at the Marcus Evans Conference in Cologne November 2012.
Views: 368 Fabian Schlage
Using Genetic Algorithms for Network Intrusion Detection and Integration into nProbe
 
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From Ignite at OSCON 2010, a 5 minute presentation by Bill Lavender: SNORT is popular Network Intrusion Detection System (NIDS) tool that currently uses a custom rule based system to identify attacks. This presentation emphasizes on writing the algorithm to write generate the rules through GA and the integration of them into nProbe, a similar network monitoring tool written by Luca Deri with a plug-in architecture. Genetic Algorithms are dependent upon identifying attributes to describe a problem and evolving a desired population. In this case, the problem is an attack through the network and identifying the attack through connection property attributes. Genetic Algorithms depends upon training data. DARPA datasets provide training data, in categorized format (attack vs. normal) along with a corresponding raw network recorded format called tcpdump. nProbe has a plug-in architecture allowing for customization. This presentation explains original code in C to evolve rules. It uses the same chromosome attributes used by Gong. The development verifies and contrasts against the research performed by Gong. It also presents the code for integration into nProbe. Don't miss an upload! Subscribe! http://goo.gl/szEauh Stay Connected to O'Reilly Media by Email - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 2319 O'Reilly
back propagation in ANN ( in hindi)
 
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very simple explanation about Backpropagation in ANN in hindi.
Views: 11627 Red Apple Tutorials
Artificial Neural Networks Explained !
 
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Contact me on : [email protected] Neural Networks is one of the most interesting topics in the Machine Learning community. Their potential is being recognized every day as the technology is advancing at an ever growing rate. From being a topic of research for decades to practical use by thousands of organizations, Neural Networks have come a long way. Today there are a number of jobs available in Machine Learning from application to research domain. But Machine Learning is not like conventional programming. It requires a different line of thinking than what conventional programming has taught us.  This might become a problem for people interested in learning Machine Learning. A lot of mathematical concepts are deeply embedded in ML and an understanding of these core concepts will help anyone starting with ML go long way ahead. Trust me! thats the only way. In this video I have tried to make those core concepts a little bit clearer by using a real-life example. This video is about how simply you can understand the working of an Artificial Neural Network. There are a lot of questions which can come to your mind after watching this video, but do not focus on the "WHY" as much as on the "HOW" of what has been explained. A detailed explanation of each of the mentioned terms will be covered in the future videos.
Views: 37265 Harsh Gaikwad
Лекция Дмитрия Коробченко по Deep Learning
 
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Дмитрий Коробченко — руководитель проектов и инженер в области машинного обучения, компьютерного зрения и обработки сигналов в Исследовательском Центре Samsung. В своей лекции Дмитрий рассказывает о том, что такое машинное обучение, нейронные сети, Deep Learning, о том, какие задачи решают современные технологии машинного обучения и ждет ли нас в будущем настоящий искусственный интеллект.
Views: 7039 E-Contenta
introduction to fuzzy logic part 1 in hindi
 
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Introduction to fuzzy logic in hindi
Views: 2023 Red Apple Tutorials
Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka
 
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( TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow ) This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail. Below are the topics covered in this tutorial: 1. Why Neural Networks? 2. Motivation Behind Neural Networks 3. What is Neural Network? 4. Single Layer Percpetron 5. Multi Layer Perceptron 6. Use-Case 7. Applications of Neural Networks Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world. Please write back to us at [email protected] or call us at +91 88808 62004 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 50152 edureka!
Managing Privacy Of Sensitive Attributes Using MFSARNN Clustering With Optimization Technique
 
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Title: Managing Privacy Of Sensitive Attributes Using MFSARNN Clustering With Optimization Technique Domain: Data Mining Key Features: 1. The slicing algorithm partitions the data into vertical and horizontal columns. The attributes and tuples in the slicing algorithm is clustered based on their similarity. In vertical partitioning, the attributes are grouped by Modified Fully Self Adaptive Resonance Neural Networks (MFSARNN). 2. The cluster formation has been improved by Genetic Algorithm based feature selection. In the horizontal partition, the tuples are grouped by Meta heuristic Fireflies Algorithm with Minkowsi Distance Measure (MFAMD). In this way the proposed system overcomes the privacy threats such as Identity disclosure, Attribute disclosure and Membership disclosure. 3. Slicing is one of the Anonymization techniques which is used to improve the privacy in data publishing. The slicing algorithm has been working with three different phases like, attribute partitioning, column generalization and tuple partitioning. The slicing algorithm partition the data set into vertical partition and horizontal partition. 4. Cluster analysis is the process of grouping the related information together, which is used for further processing and classification. Clustering techniques work with the high dimensionality of the data and produces the best result without losing the data and it also utilizes the data for entire processing. For this reason, slicing algorithm is working with clustering techniques. Clustering of attributes in the vertical partition is done by using Modified Fully Self Adaptive Resonance Neural Network (MFSARNN). 5. The working of MFSARNN as follows. Initially the sensitive features are selected by using a feature selection method. So, in the proposed system Genetic Algorithm is used for feature selection with three different steps like selection, crossover and mutation. In GA fitness function is one of the most important parameter which is used to select the sensitive features from the data. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2016 – 2017 data mining projects 5. 2016 – 2017 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2016 – 2017 ieee titles 8. 2016 – 2017 base paper 9. 2016 – 2017 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2016 – 2017 data mining weka projects 13. 2016 – 2017 b.e projects 14. 2016 – 2017 m.e projects 15. 2016 – 2017 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2016 – 2017 ieee base paper free download 23. 2016 – 2017 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2016 - 2017 48. 2016 - 2017 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects
Working of Genetic Algorithm in Hindi Part 3
 
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This tutorial is about Traveling salesman problem example solution using genetic algorithm
Views: 8112 Red Apple Tutorials
Data Mining Final Project - Crime Aware - Demo
 
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This is the demo video for crime aware
Views: 452 kuma Dm
Final Year Projects | Credit card Fraud Detection using HMM
 
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Final Year Projects | Credit card Fraud Detection using HMM More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 7513 ClickMyProject
Genetic Algorithm in MATLAB
 
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Genetic Algorithm in MATLAB using Optimization Toolbox. I discussed an example from MATLAB help to illustrate how to use ga-Genetic Algorithm in Optimization Toolbox window and from the command line in MATLAB program. Amr Abdelnaser 3rd Year (C&E)EE Dept. Sohag University, Sohag, Egypt
Views: 129089 Amr Abdelnaser
GA Based Selective Harmonic Elimination for Multilevel Inverter with Reduced Number of Switches
 
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Secure Mining of Association Rules in Horizontally Distributed Databases
 
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We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton [18]. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. [8], which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-party algorithms — one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. Our protocol offers enhanced privacy with respect to the protocol in [18]. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost.
Views: 1639 Susmay FOURGREENSOFT